A robust Collaborative Filtering approach based on user relationships for recommendation systems

Min Gao, Bin Ling, Quan Yuan, Qingyu Xiong, Yanyan Yang

    Research output: Contribution to journalArticlepeer-review

    72 Downloads (Pure)

    Abstract

    Personalized recommendation systems have been widely used as an effective way to deal with information overload.The common approach in the systems, item-based collaborative filtering (CF), has been identified to be vulnerable to “Shilling” attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first.The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the
    calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-based kNN (k Nearest Neighbor) CF approach.
    Original languageEnglish
    Article number162521
    JournalMathematical Problems in Engineering
    DOIs
    Publication statusPublished - 19 Feb 2014

    Fingerprint

    Dive into the research topics of 'A robust Collaborative Filtering approach based on user relationships for recommendation systems'. Together they form a unique fingerprint.

    Cite this